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Remove the MLContext.compute() method #795

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merged 3 commits into from
Dec 2, 2024

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@a-sully a-sully commented Nov 27, 2024

Fixes #791. Made possible by #787 which specified dispatch()

The changes proposed in #786 have also been rolled into this PR, since they can't really be decoupled from compute() removal at this point

I've tried to keep this PR minimal and focused on removing algorithms and IDL definitions which are no longer relevant. The spec still contains lots of normative text which was written with compute() in mind which doesn't map as well to dispatch() - specifically text which contains assumptions about how graph execution relates to compute devices. I've lightly updated some of this text where necessary to match the dispatch() paradigm, but my intention is to avoid opening the can of worms that is #749 in this PR :)


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a-sully commented Nov 27, 2024

@huningxin @fdwr PTAL?

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LGTM

This does make me realize that as part of #529 we'll likely need to define queuing for operations on the timeline that share tensors, to avoid racy behavior. With ArrayBuffers transferred away synchronously during compute(), there wasn't the possibility for multiple compute()s to compete for access to the buffer or script to read/write the contents.

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LGTM with a comment to example, thanks!

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LGTM, thanks!

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a-sully commented Dec 2, 2024

Thanks for the reviews, @huningxin and @inexorabletash

@fdwr PTAL?

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👍 LGTM Mr. Sully.

@fdwr fdwr merged commit 4e80205 into webmachinelearning:main Dec 2, 2024
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github-actions bot added a commit that referenced this pull request Dec 2, 2024
SHA: 4e80205
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@a-sully a-sully deleted the delete-compute branch December 2, 2024 19:52
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Remove MLContext.compute()
4 participants